Melissa I. Pardi
Larisa R. G. DeSantis- 1Illinois State Museum, Springfield, IL, United States
- 2Department of Biological Sciences, Vanderbilt University, Nashville, TN, United States
- 3Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, TN, United States
Introduction: The end Pleistocene was a time of considerable ecological upheaval. Recent work has explored the megafauna extinction’s role in altering ecosystem processes. Analyses of functional traits withing communities reveal hidden consequences of the megafauna extinction beyond declines in taxonomic diversity. Functional diversity analyses offer new insight into our understanding of past ecosystems and may even inform future rewilding efforts. However, the utility of functional diversity may be hampered by the use of discrete, taxon-level functional traits, such as dietary categories, that mask variation in functional diversity over space and time.
Methods: We present an approach in which species distribution modeling, in Maxent, provides context for interpreting variation in two widely used proxies for diet among fossil taxa: stable isotope analysis and dental microwear texture analysis. We apply this approach to two ecologically distinct taxa, the American mastodon (Mammut americanum) and mammoths (Mammuthus) and investigate their resource use over space and time from the last glacial maximum to the end Pleistocene (25–11.7 thousand years before present).
Results: Mammoth dietary behavior varies by context across their geographic distribution, despite possessing evolutionary adaptations that facilitate grazing. Mammoths exhibit a preference for grazing where species distribution modeling predicts the highest likelihood of occurrence but engage in more mixed-feeding outside of core likelihood areas. In contrast, dietary preferences for mastodon are less resolved and our analyses were unable to identify significant differences in diet across their distribution.
Discussion: The ecological roles of some species are context specific and need to be critically evaluated when planning for management of reintroductions or introducing novel species to restore lost ecological function.
Introduction
The end Pleistocene was a time of considerable environmental upheaval (Rule et al., 2012; Malhi et al., 2016), with the extinction of most megafauna (species weighing > 45 kg) (Lyons et al., 2004) occurring on the backdrop of a rapidly changing climate (Severinghaus et al., 1998; Alley, 2003). While the loss of megafauna reduced taxonomic richness more broadly, recent work has explored the megafauna extinction’s role in altering ecosystem processes through the analysis of functional traits withing communities. These studies have found that not only does functional diversity decline (Davis, 2017), but resilience declines with the loss of megafauna that filled unique ecological roles (Hedberg et al., 2022), highlighting the hidden consequences of species diversity loss. This work is timely, as the idea to rewild landscapes with so-called modern “functional equivalents” of Pleistocene megafauna is increasingly presented as a viable solution for restoring degraded ecosystems (Donlan et al., 2006; Svenning et al., 2016).
The search for modern functional equivalents relies on having a clear understanding of the biological roles of extinct species, and therein lies the rub. Much of our understanding of the ecological function of extinct animals has been inferred through comparisons with living analogs (Janis and Ehrhardt, 1988; Janis, 1995; Mendoza et al., 2002) and community-level paleoecological analyses are often conducted using discrete taxon-level traits and characteristics (Gladstone-Gallagher et al., 2019; Hedberg et al., 2022). Trait resolution can affect interpretation of functional structure (Kohli and Jarzyna, 2021) and the use of coarse categorizations, such as dietary group assignments (e.g., “grazer,” “mixed-feeder,” “browser,” “omnivore,” and “carnivore”) also does not account for variation in behavior within communities or across a taxon’s geographic distribution. By ignoring variation, we risk limiting our understanding of the ecology of extinct species and masking community-level differences in functional diversity (Violle et al., 2012), which poses a hindrance to the possibility of rewilding.
Here, we ask how dietary function varies over the distributions of two iconic ice age taxa: mammoth (Mammuthus) and American mastodon (Mammut americanum). There are many reasons that we expect landscape-scale patterns in intraspecific variation in diet. Within the niche of a species, and reflected by its geographic range, there are conditions that are most optimal for persistence and less optimal conditions near boundaries (Hutchinson, 1957). Variation in niche fitness is reflected in patterns of population densities across geographic ranges, which exhibit a pattern of central tendency across many organisms (Brown, 1984). Additionally, there is an interrelatedness between the niche, environmental conditions, and patterns of biological responses such as predation and competition (Macarthur and Levins, 1967; Maguire, 1973). Because the types and abundance of plants are constrained along gradients of temperature and precipitation (Whittaker, 1967) we predict that variation in the dietary behavior of mammoth and mastodon follow climate, and that diets consumed in the most environmentally suitable parts of the distribution, or “core areas,” differ from diets consumed closer to geographic boundaries, or “edges” (Hutchinson, 1957; Maguire, 1973; Brown, 1984).
Individual-level trait data can provide useful information regarding variation in ecological function between individuals, across populations, and across landscapes. Stable isotope analysis (SIA) of δ13C from enamel (δ13Cenamel) is an individual-level proxy for the relative consumption of C3- and C4-based resources (e.g., Cerling et al., 1997; MacFadden et al., 1999; Secord et al., 2012; DeSantis et al., 2019). In environments where C4 grasses are favored, δ13Cenamel can differentiate between the consumption of C4 grass and C3 browse to quantify degrees of browsing, mixed-feeding, and grazing behavior in herbivores (Teeri and Stowe, 1976; Cerling et al., 1998) and quantify variation across these feeding strategies within a taxon (Pardi and DeSantis, 2021; DeSantis et al., 2022).
Often as a complement to SIA, dental microwear texture analysis (DMTA) is another tool that can provide information on the diets of individual animals (DeSantis, 2016). DMTA using scale-sensitive fractal analysis measures the following attributes: anisotropy (epLsar), complexity (Asfc), textural fill volume (Tfv), and heterogeneity of complexity compared among surfaces in a 3 × 3 grid (HAsfc3) and in a 9 × 9 grid (HAsfc9) (Ungar et al., 2003; Scott et al., 2005, 2006; Scott, 2012; DeSantis, 2016). Through DMTA, microscopic tooth wear is used to characterize diets of differing hardness and toughness (Scott et al., 2006). High anisotropy is characteristic of individuals consuming tough food item such as flesh, in the case of carnivores, or grass, in the case of herbivores. High complexity distinguishes individuals consuming hard and brittle food items, while low complexity is characteristic of consuming soft items. In herbivores, DMTA has been successfully used to differentiate between diets ranging from obligate grazers to browsers to frugivores (Scott, 2012), including diets that are isotopically similar (DeSantis, 2016; DeSantis et al., 2017).
Our aim is to place intraspecific variation from SIA and DMTA into a geographic context to identify landscape-scale patterns in diet of mammoths and mastodons. Species distribution modeling (SDM) has been increasingly used to study the distribution of species over space and time, especially in response to climate change, including among fossil taxa (Martínez-Meyer et al., 2004; Elith and Leathwick, 2009; Maguire and Stigall, 2009; Wang et al., 2021). Such analyses leverage the availability of detailed paleoclimate reconstructions (e.g., Collins et al., 2006; Brown et al., 2018) as well as fossil occurrence data that are now widely accessible through databases (e.g., Williams et al., 2018). Presence-only methods, such as Maxent, are especially useful for analyzing the likely distributions of extinct species for which occurrences can be verified, but absences are uncertain or unknown (Phillips and Dudík, 2008; Elith et al., 2011). By modeling the distribution of species, relationships between climatic variables and likelihood of occurrence can be established, and geographic regions can be assessed as being more or less, suitable to a taxon.
Species distribution modeling and individual-level dietary proxies are useful tools for understanding the ecology of species. Here, we combine these approaches to explore how multiple aspects of the niche contribute to dietary variation. Mammoth and mastodon were selected for this study because they are broadly distributed, are well represented in the late Quaternary fossil record, and have diets that have been well described. Mammoth have morphological adaptations (Maglio, 1972) that permit a broad diet ranging from mixed-feeding to grazing (Smith and DeSantis, 2018, 2020; Pardi and DeSantis, 2021; DeSantis et al., 2022). In contrast, mastodon are browsers with a more narrow breadth in δ13C (Green et al., 2017; Smith and DeSantis, 2018, 2020; Pardi and DeSantis, 2021; DeSantis et al., 2022), but the type of browse that is consumed can come from a variety of sources (Lepper et al., 1991; Newsom and Mihlbachler, 2006) and can vary over time and space (Green et al., 2017). Our approach is to model their distributions using Maxent, and then compare variation in dietary proxies (via SIA or DMTA) over space. We ask if the diets of mammoth and mastodon living in the core areas of their distributions (and niches) differ from those living closer to the edges.
Materials and methods
Training and tuning of species distribution models
Our study treats mammoth (Mammuthus) at the genus level and mastodon (Mammut americanum) at the species level. This choice was deliberate as a means to be congruent with (1) the treatment of dietary proxy data in the literature and (2) what genetic studies indicate is reasonable treatment of these taxa. Much of the dietary proxy data available are presented at the genus level. This is true not just for mammoth and mastodon, but across herbivores more broadly (see Pardi and DeSantis, 2021). Multiple species are currently recognized within Mammuthus, but study of their genetics casts doubt on current species designations; while there is phylogeographic structure in mammoth matrilines, there is also introgression, potentially extensive, between nominal mammoth species and there are non-linear associations between genetics and morphological attributes that have been used to distinguish mammoth taxa (Enk et al., 2016). Mastodon taxonomy suffers in a different manner. While all of the mastodon records in our study are M. americanum and exclude M. pacificus (Dooley et al., 2019), recent genetic analyses of M. americanum identifies six distinct clades across the North American continent (Karpinski et al., 2020). Thus, taxonomic revisions to split Mammut may be in order and the taxonomic resolution of our analyses between mammoth and mastodon are comparable.
Records identified as Mammuthus and Mammut americanum were downloaded from the Neotoma Database1 (Williams et al., 2018) and supplemented with a literature search (Supplementary Table 1). A record was included as an occurrence in the Maxent model if its location could be estimated with at least 20 km precision and if a high-quality date was made directly on the taxon of interest (Barnosky and Lindsey, 2010), or if there was reasonable stratigraphic evidence to accept an associated age. Radiocarbon dates were calibrated using the Intcal20 calibration curve (Reimer et al., 2020) using the “calibrate” function in the rcarbon package (version 1.4.3) in R (version 4.2.0) (Crema and Bevan, 2021; R Core Team, 2022). Median ages were used to place occurrences into one of the following time bins: the Last Glacial Maximum (LGM; 25,000–18,000 yr B.P.), Heinrich Stadial 1 (HS1; 17,000–14,700 yr B.P.), Bølling-Allerød (BA; 14,700–12,900 yr B.P.), and the Younger Dryas Stadial (YD; 12,900–11,700 yr B.P.).
A species distribution model was trained in Maxent2, version 3.4.4 (Phillips et al., 2020) for each taxon using collective occurrences spanning the time periods of our study. Each occurrence was spatially associated with raster coverages of 19 bioclimatic variables modeled for the YD, BA, and HS1 from Paleoclim (Brown et al., 2018), and the LGM (ca. 21,000 yr B.P) from the CHELSA algorithm (Karger et al., 2017). Climate rasters had a resolution of 20 km, and occurrences were spatially thinned such that no grid cell was shared by two or more occurrences within the same time bin to reduce potential issues of autocorrelation (Legendre, 1993).
Background points in the Maxent model represent the areas accessible to a species and the climatic conditions that are present at those locations. The experimentally determined minimum number of background points required to represent the available environment is 10,000 (Phillips and Dudík, 2008). Temporally balanced background points were randomly sampled by location and time period from within a seven-degree buffer surrounding each thinned occurrence, with the proportion of background points selected to match the proportion of occurrences in each time bin (Pendleton et al., 2012; Pardi and Smith, 2016). Background points were not sampled from regions covered by glaciers or large lakes (Dyke et al., 2003).
Model tuning was conducted using the function “ENMevaluate” in the R package ENMeval (version 2.0.3) (Kass et al., 2021) and followed a “n−1” or “leave one out” jackknife procedure (Shcheglovitova and Anderson, 2013) varying two Maxent settings that regulate model complexity: feature class and regularization multiplier. Each omitted occurrence was used as a test case for each model trained with n−1 occurrences. Models were run with combinations of linear, hinge, linear with quadratic, and linear with hinge and quadratic features, and regularization multipliers ranging from 0.5 to 5.0, at 0.25 intervals. We compared a total of 76 model combinations across n iterations, each, for mammoths and mastodon. The average test omission [using the tenth percentile training presence threshold (TPT)] and average area under the receiver operating characteristic curve (AUC) across model runs was calculated for each combination. AUC is the probability that a random training occurrence will be ranked higher than a random background point (Phillips and Dudík, 2008; Elith et al., 2011). The combination that (1) minimized average omission rates and then (2) maximized average AUC values was selected for each taxon to minimize overfitting (Shcheglovitova and Anderson, 2013) while maximize predictability.
To model likely distributions for each time bin and identify geographic areas of differing likelihood of occurrence, the tuned models were projected onto gridded climate variables for each time period (Dyke et al., 2003; Karger et al., 2017; Brown et al., 2018) and categorized at different likelihood thresholds. Grid cells with modeled likelihood values above the TPT were categorized as “Core” areas. Cells below the TPT were categorized as “Edge” areas. For purposes of discussion, we further subdivided Edge areas as “intermediate” and of “lowest/least” likelihood of occurrence using the minimum training threshold (MTT). The aim of this classification was to identify geographic locations of differing likelihoods of occurrence for each time period. Multivariate environmental similarity surfaces (MESS) were used to omit predicted areas from further analyses that fell outside of the range of climate variables used to train the models (Elith et al., 2010).
Spatial analyses of dietary proxies across regions of differing suitability
To assess how mammoth and mastodon diets varied across their niches and distributions, published dietary proxies were collected from the literature, and were then spatially and temporally compared across Core areas and Edge areas as defined by the Maxent models. Our expectation is that mammoth and mastodon vary their diets according to the suitability of the environment, as regions of the highest suitability may have greater availability of preferred resources.
Prior analyses of δ13Cenamel have identified high variation within mammoth and characterized them as grazers that can mixed-feed; in contrast, there is low variation in δ13Cenamel within mastodon, which are browsers (Figure 1). SIA is, therefore, unlikely to capture significant landscape-scale differences in dietary behavior within mastodon. However, DMTA has identified significant differences in the consumption of distinct browse resources by mastodons across sites of differing vegetation types (Green et al., 2017; Smith and DeSantis, 2018). We, therefore, focus our analyses of spatial variation in diets using SIA of mammoth and DMTA of mastodon. Mammoth SIA and mastodon DMTA samples were from published georeferenced specimens that had ages confirming they were from the latest Pleistocene, after the LGM (Supplementary Tables 2, 3).
Figure 1. Isotopic breadth of mammoth (Mammuthus) and mastodon (Mammut americanum) during the Late Pleistocene (25–11.7 ka). Data are from latitudes below 37° North (MacFadden and Cerling, 1996; Connin et al., 1998; Koch et al., 1998, 2004; Hoppe, 2004; Hoppe and Koch, 2006; Vetter, 2007; Metcalfe et al., 2011; Lundelius et al., 2019; Pardi and DeSantis, 2021; DeSantis et al., 2022). Values are calibrated to the Vee Pee Dee Belemnite (V-PDB) standard.
We compared δ13Cenamel values sampled from mammoth between Core and Edge areas. Analyses were geographically restricted to samples collected from occurrences from below 37° latitude (MacFadden and Cerling, 1996; Connin et al., 1998; Koch et al., 1998, 2004; Hoppe, 2004; Hoppe and Koch, 2006; Vetter, 2007; Metcalfe et al., 2011; Lundelius et al., 2019), where δ13Cenamel values more directly reflect the relative consumption of C3 browse and C4 grass resources. Specifically, C3 vegetation is likely trees, forbs, and shrubs (δ13Cenamel values < −9‰) and C4 resources are likely grasses (δ13Cenamel values > −2‰) below 37° latitude (Teeri and Stowe, 1976; Cerling et al., 1997; Kohn, 2010). The temporal resolution of some SIA samples was coarser than our niche models, with the age estimates of some localities spanning time bins. We retained less temporally refined samples where the age estimate spanned time bins if they had the same suitability classification (Core vs. Edge). However, if a site spanned time bins where modeled suitabilities were different, the sample was removed from our analyses. The null hypothesis is that isotopic samples from Core areas and Edge areas are from the same distribution and indistinguishable.
Similarly, we compared DMTA values from mastodon across suitability regions. We did not employ any geographic constraints in these analyses; however, temporal constraints were treated the same as in mammoths. Higher complexity (Asfc) would be indicative of consuming harder foods, high anisotropy (epLsar) indicates softer and tougher foods, and low heterogeneity (HAsfc3, HAsfc9) may indicate specialized browsing (Scott et al., 2005; Scott, 2012). The null hypothesis is that DMTA parameters measured from Core area samples and Edge samples are from the same distribution and indistinguishable.
Results
Occurrences and model tuning
A literature search of occurrences with dates resulted in n = 70 occurrences of Mammuthus (n = 16, 10, 28, and 16 for the LGM, HS1, BA, and YD, respectively) and n = 37 occurrences of Mammut americanum (n = 4, 2, 26, and 5, respectively) (Supplementary Table 1). The model that minimized average omission rates and maximized average validation AUC values for each taxon was selected: for mammoth, linear features with a regularization multiplier of 4.5 resulted in an average omission rate of 0.1142 and average validation AUC of 0.6661; for mastodon, hinge features and a regularization multiplier of 2.75 resulted in an average omission rate of 0.1111 and average validation AUC of 0.7948. Given these parameter settings, the final model training AUCs were 0.6842 for mammoths and 0.8436 for mastodon. An AUC of 0.7 or higher is generally considered good, however, a lower AUC may be reflective of greater difficulty in distinguishing suitable and unsuitable habitat for widespread and more generalist species (Dobrowski et al., 2011). The environmental variable with the greatest percent contribution to the mammoth model was mean temperature of the driest quarter (85.5% contribution; Supplementary Table 4). The environmental variables with the greatest percent contribution to the mastodon model were mean temperature of the driest quarter (46.3%), maximum temperature of the warmest month (15.4%), precipitation of the coldest quarter (14.6%), and precipitation of the driest month (12.1%; Supplementary Table 5).
Modeled suitability of mammoths and interpretation of the dietary niche using stable isotope analysis
Models were projected onto climate raster layers to estimate where suitable regions for mammoth existed for each time bin in our study and classified into Core and Edge areas (Figure 2). Edge areas were further subdivided into areas that were of intermediate and lowest likelihood of occurrence. The tenth percentile training threshold was 0.3424 and the MTT was 0.2218. Projections onto the five time periods of the study indicate changes in the distribution of areas of relative likelihood (Figure 2). With the retreat of glaciers, the leading edge of Core areas for mammoth expand north but the trailing edge is displaced by Intermediate and Least Likely areas in the south, southwest, and along the west coast.
Figure 2. Modeled distributions for mammoth (Mammuthus) during the Last Glacial Maximum (A), Heinrich Stadial 1 (B), Bølling-Allerød (C), and Younger Dryas (D). Core areas are indicated in black, Edge areas are subdivided into Intermediate (medium gray) and Least Likely (light gray) categories. Occurrences used in training the Maxent models are indicated by crosses. Stable isotope analysis (SIA) sample locations are indicated as circles. Note that when a location for dietary proxy samples has age estimates spanning time bins it has been mapped across those bins for visualization purposes. Pollen records referenced in the text (stars) are labeled with their Neotoma site ID.: Lake Tulane (2570), Camel Lake (324), Montezuma Well (1710), and Bear Lake (10000). The extent of North American ice sheets are shown for 18,000, 14,500, 13,000, and 11,500 radiocarbon years B.P. following Dyke et al. (2003).
To test whether mammoth diets varied predictably with likelihood of occurrence, δ13Cenamel values were categorized as being either from Core areas or Edge areas by age and location (Figures 2, 3). The Maxent model correctly predicted SIA sample locations as places of occurrence for mammoth: none of the SIA samples were found to be from areas modeled to be least likely, or below the MTT of the model. Within Core areas δ13Cenamel values ranged from −8.7 to 0.5‰ with a median value of −1.5‰ (n = 32, nsites = 10; Table 1). Samples from Edge areas ranged from −9.0 to −0.9‰ with a median value of −2.8‰ (n = 17, nsites = 11; Table 1). δ13Cenamel sampled from Core areas were significantly less negative and reflective of the consumption of proportionally more C4 resources (Wilcoxon rank sum and signed rank test; W = 149, p = 0.01004; Figure 3). To explore the possible effects of a larger sample size from Core areas, we applied a bootstrap analysis and plotted the distribution of resulting p-values from the Wilcoxon rank sum and signed rank test (Supplementary Figure 1). This analysis produced a median p-value of 0.03 and an interquartile range from 0.01 to 0.05, and we reject the null hypothesis on this basis.
Figure 3. Boxplots of δ13Cenamel from mammoth (Mammuthus) collected below 37° latitude. Samples from Core areas of the distribution (highest likelihood of occurrence) are compared to those in Edge areas (intermediate and lowest likelihood of occurrence). Raw values are plotted in addition to the distributions given by the boxplots. The dashed green line indicates a δ13Cenamel value of –2.0‰ which is the threshold between a mixed-feeding versus grazing diet. Samples from Core areas are significantly less negative (Wilcoxon rank sum and signed rank test; W = 149, p = 0.01004).
Table 1. Summary statistics of δ13Cenamel for mammoth (Mammuthus) in this study.
Modeled suitability of mastodon and interpretation of the dietary niche using dental microwear texture analysis
Models were projected to estimate where regions for mastodon existed for each time bin in our study and classified into Core and Edge areas (Figure 4) using the tenth percentile training threshold (0.4162). Edge areas were further subdivided into areas that were of Intermediate and Lowest Likelihood of occurrence using the MTT (0.1457). Projections onto the five time periods of the study indicate changes in the distribution of areas of relative likelihood (Figure 4). With the retreat of glaciers, the leading edge of Core areas for mastodon move northwards, while the trailing edge is displaced by Intermediate and Least Likely areas across the south and west.
Figure 4. Modeled distributions for mastodon (Mammut americanum) during the Last Glacial Maximum (A), Heinrich Stadial 1 (B), Bølling-Allerød (C), and Younger Dryas (D). Core areas are indicated in black, Edge areas are subdivided into Intermediate (medium gray) and Least Likely (light gray) categories. Occurrences used in training the Maxent models are indicated by crosses. Dental microwear texture analysis (DMTA) sample locations are indicated as circles. Note that when a location for dietary proxy samples has age estimates spanning time bins it has been mapped across those bins for visualization purposes. Pollen records referenced in the text (stars) are labeled with their Neotoma site ID.: Brewster Creek (9588), Appleman Lake (10003), Stotzel-Leis Site (2520), Cater Site (337), Camel Lake (324), and Lake Tulane (2570). The extent of North American ice sheets are shown for 18,000, 14,500, 13,000, and 11,500 radiocarbon years B.P. following Dyke et al. (2003). Panel (C) inset shows Northern mid-continent data in more detail for the Bølling-Allerød.
To test whether diets of mastodon varied predictably with likelihood of occurrence, DMTA samples were categorized as being either from Core areas or from Edge areas based on their age and where they were sampled from Figures 4, 5 and compared. The Maxent model correctly predicted most of the DMTA sample locations as places of occurrence for mastodon: only one out of the 14 DMTA sample localities (Friesenhahn Cave) was from an area modeled to be least likely, meaning most were found to be at least within the MTT of the model. No significant differences were found in complexity (Asfc), anisotropy (epLsar), textural fill volume (Tfv), or heterogeneity (Hasfc3 and Hasfc9) between samples taken from Core areas (n = 32, nsites = 12; Table 2) and Edge areas (n = 10, nsites = 2; Table 2). While a Wilcoxon rank sum and signed rank test of Asfc failed to reject the null hypothesis when evaluated at α = 0.05 (W = 100, p = 0.0788), samples from Core areas had Asfc values that ranged from 0.537 to 5.926 and had a median value that was higher (1.904) than samples from Edge areas ranging 0.429 to 2.759 (median = 1.055; Figure 5). A comparison of the summary statistics across DMTA textures suggests that individuals are most differentiated by complexity (Asfc) over space when compared to any other texture variable (Table 2).
Figure 5. Boxplots of epLsar (A), Asfc (B), Tfv (C), Hasfc3 (D), and Hasfc9 (E) for mastodon (Mammut americanum). Individuals sampled from Core areas of the distribution (highest likelihood of occurrence) are compared to those in Edge areas (intermediate and lowest likelihood of occurrence). Raw values are plotted in addition to the distributions given by the boxplots. A Wilcoxon rank sum and signed rank test found no significant differences between groups for any dental microwear texture analysis (DMTA) texture parameter.
Table 2. Summary statistics of dental microwear texture analysis (DMTA) texture attributes for mastodon (Mammut americanum) in this study.
Discussion
Rather than attempt to strictly define geographic ranges of mammoth (Mammuthus) and mastodon (Mammut americanum), which SDMs often over- or under-predict (Mellert et al., 2011; Marcer et al., 2013; Lee-Yaw et al., 2022), our aim was to compare dietary function within Core areas of greatest likelihood to dietary function in Edge areas of lower likelihood (Peterson et al., 2018; Figures 2, 4). We established correlative relationships between climatic variables and likelihood of occurrence to test the hypothesis that mammoth and mastodon consume variable diets at the landscape scale according to relative suitability of the environment using the maximum-entropy approach of species distribution modeling. We found evidence for significant dietary preferences of mammoth (Figure 3 and Table 1), while preference in mastodon was less resolved (Figure 5 and Table 2).
Despite the cosmopolitan distribution of mammoth and their apparent high variation of dietary breadth (Smith and DeSantis, 2018), greater consumption of grass in Core areas across their modeled distribution suggests a preference for grass (Figure 3) as would be expected from both their morphology (Maglio, 1972) and as a requirement for adequate rates of tooth wear (Fagan et al., 1999). These preferences resemble those of Elephas maximus (Asian elephants) which are variable mixed-feeders with a preference for grazing (Sukumar et al., 1987; Baskaran et al., 2010; Koirala et al., 2016).
As a dietary proxy, δ13Cenamel can discriminate between browse and graze consumption at low latitudes (below 37°) because most low-latitude grasses use the C4 photosynthetic pathway and have tissues that are isotopically distinct from those of C3 trees, forbs, and shrubs (Teeri and Stowe, 1976; Paruelo and Lauenroth, 1996; Cerling et al., 1997, 1998; Macfadden et al., 1999). High δ13Cenamel values from individuals sampled at low latitudes in our study indicate greater consumption of C4 grass by mammoths where they are most likely to occur. Preference is exhibited when a resource is utilized at a higher frequency than it occurs, and these individuals are from landscapes of mixed, but C3-browse dominant, resources. Local pollen records from Camel Lake (Watts et al., 1992; Wang et al., 2019) and Lake Tulane (Grimm et al., 1993; Wang et al., 2019; Figure 2) confirm the presence of mixed-parkland and deciduous forest environments coincident with SIA samples during Heinrich Stadial 1 through the Younger Dryas (Supplementary Table 6). Where, then, are these mammoth grazing?
Elephants typically maintain small home ranges (<250 km) (Bonhof and Pryor, 2022) but exhibit variable nomadic behavior that is influenced by seasonal change of habitat, the availability of food and water, as well as sex (Sukumar et al., 1987; Baskaran et al., 2010; Koirala et al., 2016). Similar movement patterns in mammoths have been inferred from 87Sr/86Sr analyses, although longer treks to other geographic areas are feasible (Bonhof and Pryor, 2022). If grasses are distributed heterogeneously in patches within a browse-dominated environment, mammoth could have selectively made use of these resources by traveling between patches. However, enriched δ13Cenamel values signaling the use of C4 resources indicate that grazing was primarily at lower latitudes, as the relative abundance of C3 to C4 grasses increases with latitude (Teeri and Stowe, 1976; Paruelo and Lauenroth, 1996; Cerling et al., 1997, 1998; Macfadden et al., 1999).
Mammoth sampled from Edge areas of lower likelihood made greater use of mixed C3 and C4 resources (Figure 3). These individuals may be consuming the local vegetation. Pollen from Montezuma Well (Davis and Shafer, 1992) documents predominantly desert vegetation which includes on average ∼5% diversity from grasses during Heinrich Stadial 1 through the Younger Dryas, near SIA samples in the Southwest (Figure 2 and Supplementary Table 6). Another nearby pollen site is Bear Lake (Weng and Jackson, 1999), which documents predominantly spruce parkland with on average <1% grasses and sedges during the Bølling-Allerød and the Younger Dryas. Alternatively, these individuals could be acquiring a mixed signal by consuming C3 grasses from higher latitudes: future studies of individual movement can help clarify where these animals are foraging.
Dietary preferences are less clearly defined for mastodon. Their isotopic breadth is, overall, relatively low and indicates restricted use of C3 resources (Figure 1; Pardi and DeSantis, 2021). However, analyses a coprolites demonstrate that they consumed a broad variety of C3 plants (Lepper et al., 1991; Newsom and Mihlbachler, 2006). We therefore analyzed published DMTA values to explore variance in food texture across their modeled distribution. Mastodon from Core areas of highest likelihood of occurrence have somewhat higher complexity (Asfc) values (median = 1.904, Figure 5 and Table 2) than Edge areas (median = 1.055) which hints at greater consumption of brittle or hard food items (bark, nuts, and seeds) in Core areas, although this difference is non-significant (p = 0.0788). The bulk of what is known about mastodon diets inferred through DMTA comes from a handful of studies comparing regional and temporal variation across sites of differing habitats (Green et al., 2017; Smith and DeSantis, 2018, 2020) and age demographics (Smith and DeSantis, 2018). Sampling for mastodon DMTA does not have nearly the breadth of geographic coverage as SIA for mammoth (Figures 2, 3). Future analyses of DMTA from samples collected outside of the Core areas of highest likelihood will help to clarify dietary preferences.
Although we were unable to identify significant differences in mastodon diets according to their likelihood of occurrence using DMTA, contemporaneous pollen records point to habitat types that may have been more suitable. Core areas of high likelihood of occurrence are consistently present for mastodon in the northern midcontinent during the late Pleistocene (Figure 4). Numerous pollen records from this region (Brewster Lake, Appleman Lake, Stotzel-Leis Site, and Carter Site) indicate the presence of mixed-parkland and prairie throughout the this time (Shane, 1987; Curry et al., 2007; Gill et al., 2009; Figure 4; Supplementary Table 6). This contrasts with pollen records near mastodon in Edge areas in the southeast, such as Camel Lake (Watts et al., 1992; Wang et al., 2019) and Lake Tulane (Grimm et al., 1993; Wang et al., 2019; Figure 4) which confirm the presence of mixed-parkland and deciduous forest.
When considering the scale and temporal grain of different dietary proxies, δ13Cenamel and microwear can reasonably be applied to questions of habitat tracking and behavioral plasticity (Davis and Pineda-Munoz, 2016). δ13Cenamel integrates the isotopic signal of the resources being used over the weeks to months of life during which the enamel mineralizes, which could also mean an integration of resources used across a geographic area for a highly mobile animal. For this reason, SIA is a reasonable proxy for what an animal is eating at the scale of a landscape (Davis and Pineda-Munoz, 2016) as we have applied it here. In contrast, microwear records properties of the food consumed during the days to weeks leading up to death (Grine, 1986). It therefore has high fidelity to the specific location where an individual is found as a fossil. DMTA has been applied to characterize interspecific dietary variation (Scott, 2012; DeSantis, 2016) as well as intraspecific variation over time and space (Rivals et al., 2007).
The mastodons sampled for DMTA from Friesenhahn Cave warrant further comment. These individuals are juveniles and have low complexity values (Supplementary Table 3). It is unclear if these low values represent ontogenetic niche partitioning or dietary differences that are reflective of the environment, and the question of whether there are ontogenetic shifts in mastodon diet, more generally, is still open (Smith and DeSantis, 2018). The megafaunal remains at Friesenhahn Cave span a dynamic time (15–20 ka) (Graham et al., 2013) where the region experienced a shift from high likelihood of mastodon occurrence to low likelihood (Figure 4). The local vegetation around Friesenhahn Cave is described in other studies as C4 open grassland with some riparian forests (Hoppe, 2004; Koch et al., 2004; Graham et al., 2013), consistent with our classification of this locality as outside the Core niche of mastodon.
There are refinements that could be made to our models that should be considered before any application beyond the scope of our study. High likelihood of occurrence is not equivalent to presence (Elith and Leathwick, 2009), and we do not account for physical barriers to dispersal. Doing so, however, would not qualitatively affect our results, as we are only concerned with drawing interpretations from dietary proxies from known occurrences. Our models are explicitly limited to relating climate variables with occurrence, which assumes climate is adequately correlated with resources that are required by Proboscideans (e.g., water and vegetation) (Sukumar et al., 1987; Baskaran et al., 2010; Koirala et al., 2016; Bonhof and Pryor, 2022). Realized niches are impacted by variables beyond climate, including biotic interactions (Hutchinson, 1957; Leibold, 1995; Chase and Leibold, 2009), which our models do not account for.
A potentially strong biotic interaction that warrants future investigation is competition. As some of the very largest animals on the landscape, Proboscideans function as ecosystem engineers (Owen-Smith, 1992). Today, African elephants (Loxodonta africana) compete with mesobrowsers and mesomixed feeders, but facilitate mesograzers (Fritz et al., 2002). The presence of megaherbivores, such as modern elephants, impacts the feeding of smaller sympatric herbivores; however, Pleistocene environments supported a much more diverse megaherbivore community. Were sympatric megaherbivores competing with each other, or partitioning resources? DMTA analyses of three middle/late Miocene Proboscideans (Gomphotherium subtapiroideum, Gomphotherium steinheimense, and Deinotherium giganteum) from the Molasse Basin in Southern Germany supports niche partitioning rather than competition for resources (Calandra et al., 2008). In contrast, competitive exclusion imposed by Mammut and Mammuthus is a plausible cause for the eventual extinction of gomphotheres (Cuvieronius) in North America (Smith and DeSantis, 2020).
Mammoth and mastodon sometimes do co-occur, at least regionally. In the Northern, North American mid-continent, they exhibit partial isotopic niche overlap in their use of C3 resources (Widga et al., 2021), although whether this represents consumption of C3 browse or graze within these mammoth is not resolved. The influence these taxa have on each other more generally when they share a landscape remains to be clarified. Do they partition resources, and if so, how is this impacted by geographic variation in environmental suitability as we have identified in the present study? Our models identify geographic locations where there is overlap of Core niche areas for mammoth and mastodon, as well as areas where one is more likely to occur over the other. For example, the Core area of mammoth overlaps with the Edge areas for mastodon in Florida. In contrast, there appear to be few regions where the Core area of mastodon overlaps where mammoth are at the edge of their niche. Our current dataset is not extensive enough to directly compare diets within and outside of areas of sympatry, but our models could provide context for future comparisons.
Our study is unique in that it aims to explore dietary preference and spatially explicit variation within the context of the ecological niches of extinct taxa. Dietary proxy data alone can be incredibly useful for quantifying how resources are being use by individuals and populations over time and space (MacFadden and Cerling, 1996; Scott et al., 2005, 2006; DeSantis, 2016). Our approach provides a means for contextualizing proxy data by the suitability of the environment across the geographic distribution of a taxon. This context matters because ecological interactions and factors influencing populations at geographic range margins are not the same as in the center of the range–boundaries exist because of limiting conditions preventing persistence that are not so limiting elsewhere (Hutchinson, 1957; Brown, 1984).
The approach presented here provides a means for applying distributional context to interpretation of paleoecological data, where occurrences are known but boundaries of geographic ranges are poorly defined. This may be particularly useful when considering the potential causes and consequences of extinctions across landscapes. For example, one could reasonably point to climate and environmental change as a possible cause for late Pleistocene declines and extirpation of mastodon and mammoth in the Central and Southern Great Plains, as likelihood of occurrence decreases with climate change over time (Figures 2, 4). However, losses in the Core area of their distributions may have other causes, such as hunting and landscape changes caused by humans, which is supported by synchronous patterns of extinction and the timing and trajectory of human dispersal (Surovell et al., 2005, 2016; Hamilton and Buchanan, 2007).
Analyses of functional diversity can reveal hidden consequences of taxonomic diversity loss. However, applying taxon-level traits in functional diversity analyses may mask variation that arises from plastic behavior. Specifically, dietary behavior can vary due to the non-uniform distribution of resources and biotic interactions over space. Here, we illustrate this point by exploring how diet, measured through proxies, varies over the modeled distributions of mammoths and mastodon. Mammoth dietary behavior varies by context across their geographic distribution, despite having evolutionary adaptations for grazing and exhibiting a preference for grass overall as a taxon. In contrast, specific dietary preferences for mastodon are less resolved and our analyses were unable to identify significant differences in the selection of browse across their distribution as it relates to likelihood of occurrence. The ecological roles of some species may be context specific and need to be critically evaluated when planning for the management of reintroductions or introducing novel species to restore lost ecological function.
Data availability statement
The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
MP collected data, conducted analyses, and wrote the first draft of the manuscript. LD collected data, aided in the development of the research, and helped with revisions to the manuscript. Both authors contributed to the article and approved the submitted version.
Funding
This work was funded by the National Science Foundation (EAR 1725154) and Vanderbilt University.
Acknowledgments
We are thankful to all who have contributed to the Neotoma database and all of the museum staff who have collected, curated, and managed the specimens used in this manuscript. We also thank C. Widga and A. Jukar for valuable discussions about Proboscideans and the feedback provided by two anonymous reviewers.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fevo.2022.1064299/full#supplementary-material
Footnotes
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Keywords: carbon, Maxent, Mammuthus, Mammut, stable isotope analysis (SIA), dental microwear texture analysis (DMTA)
Citation: Pardi MI and DeSantis LRG (2022) Interpreting spatially explicit variation in dietary proxies through species distribution modeling reveals foraging preferences of mammoth (Mammuthus) and American mastodon (Mammut americanum). Front. Ecol. Evol. 10:1064299. doi: 10.3389/fevo.2022.1064299
Received: 08 October 2022; Accepted: 09 November 2022;
Published: 25 November 2022.
Edited by:
Anna Maria Mercuri, University of Modena and Reggio Emilia, ItalyReviewed by:
Juan López Cantalapiedra, Museum of Natural History Berlin (MfN), GermanyEllen Schulz-Kornas, University of Leipzig, Germany
Copyright © 2022 Pardi and DeSantis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Melissa I. Pardi, bWVsaXNzYS5wYXJkaUBpbGxpbm9pcy5nb3Y=